48 resultados para ablation depth


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This article explores the possibility of using a laser to remove toner-print from office paper. Removal of print would allow paper to be re-used instead of being recycled or disposed into a landfill. This might reduce climate change gas emissions per tonne of office paper by between 45% and 95%. Although there is little previous research on the area, a number of related articles on paper conservation methods using laser radiation can be found in literature. Different authors have studied the effects of laser energy on blank paper and its application for cleaning soiled paper. However, this study examines toner-print removal from paper by laser ablation. In this article a laser in the visible range is applied to a single toner-paper combination with a range of energy fluences. Results are evaluated by means of colour measurements under the L*a*b* colour space and SEM images. Analysis of the samples reveals that there are parameters under which it is possible to remove toner from paper without causing significant discolouration or damage to the substrate. This means that it is technically possible to remove toner-print for paper re-use.

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Information is one of the most important resources in our globalized economy. The value of information often exceeds the value of physical assets. Information quality has, in many ways, an impact on asset management organisations and asset managers struggle to understand and to quantify it, which is a prerequisite for effective information quality improvement. Over the past few years, we have developed an innovative management concept that addresses these new asset management challenges: a process for Total Information Risk Management (TIRM), which has been already tested in a number of asset management industries. The TIRM process enables to manage information quality more effectively in asset management organisations as it focuses specifically on the risks that are imposed by information quality. In this paper, we show how we have applied the TIRM process in an in-depth study at a medium-sized European utility provider, the Manx Electricity Authority (MEA), at the Isle of Man.

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In an earthquake, underground structures located in liquefiable soil deposits are susceptible to floatation following an earthquake event due to their lower unit weight relative to the surrounding saturated soil. The uplift displacement of an underground structure in liquefiable soil deposit can be affected by the buried depth and size of the structure. Dynamic centrifuge tests have been carried out to investigate the influence of these factors by measuring the uplift displacement of shallow model circular structures. Ratios for the buried depth and diameter effects of the structure are introduced to compare the uplift displacement in different soil and earthquake conditions. With the depth effect and diameter effect ratios, the uplift displacement of a buoyant structure in liquefiable soil can also be estimated based on performance of similar structures in comparable soil condition and subjected to a similar earthquake event. © 2012 Elsevier Ltd.

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Laser micro machining is fast gaining popularity as a method of fabricating micro scale structures. Lasers have been utilised for micro structuring of metals, ceramics and glass composites and with advances in material science, new materials are being developed for micro/nano products used in medical, optical, and chemical industries. Due to its favourable strength to weight ratio and extreme resistance to chemical attack, glassy carbon is a new material that offers many unique properties for micro devices. The laser machining of SIGRADUR® G grade glassy carbon was characterised using a 1065 nm wavelength Ytterbium doped pulsed fiber laser. The laser system has a selection of 25 preset waveforms with optimised peak powers for different pulsing frequencies. The optics provide spot diameter of 40 μm at the focus. The effect of fluence, transverse overlap and pulsing frequency (as waveform) on glassy carbon was investigated. Depth of removal and surface roughness were measured as machining quality indicators. The damage threshold fluence was determined to be 0.29 J/cm2 using a pulsing frequency of 250 kHz and a pulse width of 18 ns (waveform 3). Ablation rates of 17 < V < 300 μm3/pulse were observed within a fluence range of 0.98 < F < 2.98 J/cm2. For the same fluence variation, 0.6 μm to 6.8 μm deep trenches were machined. Trench widths varied from 29 μm at lower fluence to 47 μm at the higher fluence. Square pockets, 1 mm wide, were machined to understand the surface machining or milling. The depth of removal using both waveform 3 and 5 showed positive correlation with fluence, with waveform 5 causing more removal than waveform 3 for the same fluence. Machined depths varied from less than 1 μm to nearly 40 μm. For transverse overlap variation using waveform 3, the best surface finish with Rz = 1.1 μm was obtained for fluence 0.792 J/cm2 for transverse overlap of 1 μm, 6 μm, and 9 μm at machined depths of 22.9 μm, 6.6 μm, and 4.6 μm respectively. For fluence of 1.426 J/cm2, the best surface finish with Rz = 1.2 μm was obtained for transverse overlap of 6 μm, and 9 μm at machined depths of 12.46 μm, and 8.6 μm respectively. The experimental data was compiled as machining charts and utilised for fabricating a micro-embossing glassy carbon master toolsets as a capability demonstration.

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Three-dimensional (3-D) spatial data of a transportation infrastructure contain useful information for civil engineering applications, including as-built documentation, on-site safety enhancements, and progress monitoring. Several techniques have been developed for acquiring 3-D point coordinates of infrastructure, such as laser scanning. Although the method yields accurate results, the high device costs and human effort required render the process infeasible for generic applications in the construction industry. A quick and reliable approach, which is based on the principles of stereo vision, is proposed for generating a depth map of an infrastructure. Initially, two images are captured by two similar stereo cameras at the scene of the infrastructure. A Harris feature detector is used to extract feature points from the first view, and an innovative adaptive window-matching technique is used to compute feature point correspondences in the second view. A robust algorithm computes the nonfeature point correspondences. Thus, the correspondences of all the points in the scene are obtained. After all correspondences have been obtained, the geometric principles of stereo vision are used to generate a dense depth map of the scene. The proposed algorithm has been tested on several data sets, and results illustrate its potential for stereo correspondence and depth map generation.

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The lack of viable methods to map and label existing infrastructure is one of the engineering grand challenges for the 21st century. For instance, over two thirds of the effort needed to geometrically model even simple infrastructure is spent on manually converting a cloud of points to a 3D model. The result is that few facilities today have a complete record of as-built information and that as-built models are not produced for the vast majority of new construction and retrofit projects. This leads to rework and design changes that can cost up to 10% of the installed costs. Automatically detecting building components could address this challenge. However, existing methods for detecting building components are not view and scale-invariant, or have only been validated in restricted scenarios that require a priori knowledge without considering occlusions. This leads to their constrained applicability in complex civil infrastructure scenes. In this paper, we test a pose-invariant method of labeling existing infrastructure. This method simultaneously detects objects and estimates their poses. It takes advantage of a recent novel formulation for object detection and customizes it to generic civil infrastructure scenes. Our preliminary experiments demonstrate that this method achieves convincing recognition results.